Quantitative Data Modeling

A quantitative data model includes many explanatory variables, including genotypes, mixture weight and data variance. These variables (and their uncertainty) are determined by a joint probability distribution over all the data and parameters. This lecture shows how using a quantitative likelihood function can preserve DNA identification information.

Mixture Weight and Inference

Statistical inference of genotypes and other DNA variables follows human intuition about the underlying biological processes, but can apply far more computing power. In this lecture, we use mixture weight as an illustrative example of how computers can refine human insight. We also show how generally accepted computer sampling can account for all the DNA variables.